Implementing a mobile communication systems (MCS) in real life environments is a challenging and complex undertaking. All together, the infrastructure and devices and techniques used to interconnect the parts of the system can be referred to as a MCS. A primary goal of MCS system designers and operators is to implement and operate the MCS system in the most reliable, robust and efficient manner so as to serve the largest number of customers with the highest level of quality at a most cost effective rate.
The complexities of designing and operating communications networks can be attributed to a number of factors. One set of factors includes the physical communication channels in the presence of urban structures, natural terrain, atmospheric variations and other environmental factors. Another set of factors arises from the engineering systems needed to support wireless communications over useful ranges, which includes the antenna designs and placements, communication base station hardware and software, wired communication infrastructure, switching and other maintenance and upkeep factors. Yet another set of factors arises from the mobile wireless devices and their sheer numbers in some areas, each requiring real-time and acceptable quality of service around the clock.
One type of MCS is a cellular telephone communication system and network, which varies from region to region but shares some physical and design and performance features. Cellular communication systems generally include a network of base stations including telephony processors and servers connected to physical antenna installations. The antenna installations permit over the air wireless communication with suitably equipped and subscribing customers. In most or all cases, a mobile communication device can continue a communication session even when traversing from one cell of the cellular network to another, using established hand-off methods. A well designed and operated cellular system offers consistent good quality communication with few loss-of-communication problems (dropped calls) or disruptions due to hand-off events, interference, fading or other noise generating factors. The settings of various controlling parameters in mobile MCS significantly affect various dimensions of performance of mobile devices, which are connected to and utilize the services provided by the MCS. Mobility Robustness Optimization (MRO), Mobility Load Balancing (MLB), Coverage and Capacity Optimization (CCO) Self-Optimization functions as applied to a heterogeneous MCS are designed to adapt network configuration to changes in user traffic and mobility patterns automatically, to provide the most efficient usage of network resources, and to ensure the best quality-of-service.
Those skilled in the art are familiar with the concept of self-optimizing networks (SON), sometimes referred to as self-organizing networks. A SON is configured using functions and by setting parameters to control the specific operation of the network. Different SON functions may overlap in control parameters, which are changed to cause the desired effect on the performance of the MCS. Conflicts in the application of such changes made by separate SON functions need to be avoided in order to prevent disruptive effect to each other. Applying multiple SON functions on the same cell or cell relation can cause network degradation.
In some existing implementations, a straightforward solution was to isolate geographical regions selected for simultaneous operation of SON optimization functions. However, this results in an isolation with a very coarse geographic granularity, and does not allow utilizing the full power of multiple optimizer functionality. Coarse granularity of optimization results in reduced efficiency of deploying SON into the MCS cellular network and the benefits obtained from utilizing SON will not justify actual cost of deploying SON solution.
In another existing implementation, strict priority-based policies between SON functions cannot provide fine-grained optimization as a highest-priority function. These implementations preempt low-priority functions. Systems with required alignment between SON functions fail to provide the abstraction for higher level configuration of the coordination between SON functions and need fine-tuning of initial settings and policies for each function. This type of implementation also increases the operator's operational expenses (OPEX) of system maintenance and administration. Additionally, these systems cannot scale well because they require definition of alignment rules of conflicting parameters for each SON function pair, which overlap in control parameters.
Prior art solutions do not provide an adequate solution to designing and operating effective SON systems and communications networks.